121 citations · 407 across the 22 of their papers we have counts for
4 papers · 1 filter
A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces
Charline Le Lan, Joshua Greaves, Jesse Farebrother +4
Many machine learning problems encode their data as a matrix with a possibly very large number of rows and columns. In several applications like neuroscience, image compression or…
Offline Q-Learning on Diverse Multi-Task Data Both Scales And Generalizes
Aviral Kumar, Rishabh Agarwal, Xinyang Geng +2
The potential of offline reinforcement learning (RL) is that high-capacity models trained on large, heterogeneous datasets can lead to agents that generalize broadly, analogously t…
Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro +2
Learning tabula rasa, that is without any prior knowledge, is the prevalent workflow in reinforcement learning (RL) research. However, RL systems, when applied to large-scale setti…
On the Generalization of Representations in Reinforcement Learning
Charline Le Lan, Stephen Tu, Adam Oberman +2
In reinforcement learning, state representations are used to tractably deal with large problem spaces. State representations serve both to approximate the value function with few p…